10th International Congress on Information and Communication Technology

Authors - Lucas V. Santos, Vitor B. Souza
Abstract - Fog computing emerges as an innovative solution for edge data processing, proving to be particularly important in the context of the Internet of Things (IoT) by delivering low latency and high bandwidth at the cost of requiring a stable connection. One application that has greatly benefited from this concept is the use of Unmanned Aerial Vehicles (UAVs), also known as drones, for various applications requiring real-time communication between these devices and, potentially, a base station. This paper focuses on the use of UAVs, highlighting the connectivity challenges posed by the limitations of wireless communication technologies, such as Wi-Fi. To address these challenges, we propose a model based on deep reinforcement learning (DDQN), which helps drones make decisions on the best route between the origin and destination, balancing the minimization of travel time and the maximization of connectivity throughout the journey. Using a simulated environment where drones are trained to avoid disconnection areas, we found that the proposed model significantly improves connection stability in areas with limited coverage, albeit with an unavoidable increase in route distance. Comparisons with traditional routing methods demonstrate the advantages of our model.